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论文（Publications）
Shi, B., Yuan, Y., Zhuang, T., Xu, X., Schmidhalter, U., Ata-UI-Karim, S. T., Zhao, B., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Improving water status prediction of winter wheat using multi-source data with machine learning. European Journal of Agronomy, 139, 126548. Doi: 10.1016/j.eja.2022.126548 (IF5=6.384, Q1)
Li, Y., Miao, Y., Zhang, J., Cammarano, D., Li, S., Liu, X., Tian, Y., Zhu, Y., Cao, W.*, Cao, Q.*, 2022. Improving nitrogen status estimation of winter wheat using random forest by integrating multi-source data across different agro-ecological zones. Frontiers in Plant Science, 13, 890892. Doi: 10.3389/fpls.2022.890892 (IF5=7.255, Q1)
Li, X.#, Ata-UI-Karim, S. T.#, Li, Y., Yuan, F., Miao, Y., Yoichiro K., Cheng, T., Tang, L., Tian, X., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Advances in the estimations and applications of critical nitrogen dilution curve and nitrogen nutrition index of major cereal crops. A review. Computers and Electronics in Agriculture, 197, 106998. Doi: 10.1016/j.compag.2022.106998 (IF5=6.817, Q1)
Ruan, G., Li, X., Yuan, F., Cammarano, D., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Improving wheat yield prediction integrating proximal sensing and weather data with machine learning. Computers and Electronics in Agriculture, 195, 106852. Doi: 10.1016/j.compag.2022.106852 (IF5=6.817 Q1)
Yuan, Y., Miao, Y., Yuan, F., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Delineating soil nutrient management zone based on optimal sampling interval in medium- and small-scale intensive farming systems. Precision Agriculture, 23: 538-558. Doi: 10.1007/s11119-021-09848-1 (IF5=5.875, Q1)
Li, S., Yuan, F., Ata-UI-Karim, S. T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2019. Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sensing, 11(15), 1763. Doi: 10.3390/rs11151763 (IF5=5.786, Q1)
Li, S., Ding, X., Kuang, Q., Ata-UI-Karim, S. T., Cheng, T., Liu., X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2018. Potential of UAV-based active sensing for monitoring rice leaf nitrogen status. Frontiers in Plant Science, 9: 1834. Doi: 10.3389/fpls.2018.01834 (IF5=7.255, Q1)
Cao, Q., Miao, Y.*, Shen, J., Yuan, F., Cheng, S., Cui, Z., 2018. Evaluating two Crop Circle active canopy sensors for in-season diagnosis of winter wheat nitrogen status. Agronomy, 8(10), 201. Doi: 10.3390/agronomy 8100201 (IF5=4.117, Q1)
Cao, Q., Miao, Y.*, Feng, G., Gao, X., Liu, B., Liu, Y., Li, F., Khosla, R., Mulla, D.J., Zhang, F., 2017. Improving nitrogen use efficiency with minimal environmental risks using an active canopy sensor in a wheat-maize cropping system. Field Crops Research, 214: 365-372. Doi: 10.1016/j.fcr.2017.09.033 (IF5=7.234, Q1)
Cao, Q., Miao, Y.*, Li, F., Lu, D., Gao, X., Liu, B., Chen, X., 2017. Developing a new Crop Circle active canopy sensor - based precision nitrogen management strategy for winter wheat in North China Plain. Precision Agriculture, 18: 2-18. Doi: 10.1007/s11119-016-9456-7 (IF5=5.875, Q1)
Cao, Q., Miao, Y.*, Shen, J., Yu, W., Yuan, F., Cheng, S., Huang, S., Wang, H., Jiang, R., Yang, W., Li H., 2016. Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor. Precision Agriculture, 17(2): 136-154. Doi: 10.1007/s11119-015-9412-y (IF5=5.875, Q1)
Cao, Q., Miao, Y.*, Feng, G., Gao, X., Li, F., Liu B., Yue, S., Cheng, S., Ustin, S., Khosla, R., 2015. Active canopy sensing of winter wheat nitrogen status: an evaluation of two sensor systems. Computers and Electronics in Agriculture, 112: 54-67. Doi: 10.1016/j.compag.2014.08.012 (IF5=6.817, Q1)
Cao, Q., Miao, Y.*, Wang, H., Huang, S., Cheng, S., Khosla, R., Jiang, R., 2013. Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154: 133-144. Doi: 10.1016/j.fcr.2013.08.005 (IF5=7.234, Q1)
Cao, Q., Cui, Z., Chen, X., Khosla, R., Dao, T. H., Miao, Y.*, 2012.Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming. Precision Agriculture, 13: 45-61. Doi: 10.1007/s11119-011-9244-3 (IF5=5.875, Q1)
李鑫格, 高杨, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2022. 播期播量及施氮量对冬小麦生长及光谱指标的影响. 作物学报, 48(4), 975-987. Doi: 10.3724/SP.J.1006.2022.11033
李鑫格, 项方林, 吴思雨, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2022. 基于植被指数时序动态的冬小麦氮素营养诊断方法. 麦类作物学报, 42(1), 109-119. Doi: 10.7606/j.issn.1009-1041.2022.01.13
史博, 马祖凯, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2022. 小麦植株水分状况遥感监测研究进展与展望. 麦类作物学报, 42(4), 495-503. Doi: 10.7606/j.issn.1009-1041.2022.04.13
项方林, 李鑫格, 马吉锋, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2020. 基于冠层时序植被指数的冬小麦单产预测. 中国农业科学, 53(18), 3679-3692. Doi: 10.3864/j.issn. 0578-1752.2020.18.005
杜宇笑, 李鑫格, 张羽, 程涛, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2020. 不同产量水平稻茬小麦氮素营养指标特征. 植物营养与肥料学报, 26(8), 1420-1429. Doi: 10.11674/zwyf.19498
杜宇笑, 李鑫格, 王雪, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2020. 不同产量水平稻茬小麦氮素需求特征研究. 作物学报, 46(11), 1780-1789. Doi: 10.3724/SP.J.1006.2020.01027
曹强, 田兴帅, 马吉锋, 姚霞, 刘小军, 田永超, 曹卫星, 朱艳*, 2020. 中国三大粮食作物临界氮浓度稀释曲线研究进展. 南京农业大学学报, 43(3), 392-402. Doi: 10.7685/jnau.201907005
田兴帅, 李松阳, 张羽, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2019. 基于临界氮浓度稀释曲线的小麦氮肥需求量估测研究. 麦类作物学报, 39(9), 1112-1120. Doi: 10.7606/j.issn.1009-1041.2019.09.12
专著（Book Chapters）
曹卫星, 程涛, 朱艳, 姚霞, 倪军, 田永超, 刘小军, 汤亮, 曹强, 张小虎, 邱小雷, 郑恒彪. 作物生长光谱监测. 北京: 科学出版社. 2020
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